Method for quantifying the number of free fibers emanating from a surface

ABSTRACT

The present disclosure provides a method for counting the number of fibers emanating from the surface of a web substrate.

FIELD OF THE INVENTION

This disclosure relates generally to an image analysis method forquantifying the number of fibers that emanate from the surface of a websubstrate. This disclosure more generally relates to determining thenumber of fibers that a distal end of a portion of a fiber that isunattached to an underlying support structure such as facial, bath, andpaper toweling.

BACKGROUND OF THE INVENTION

Market research has shown that “softness” is a property of paper-basedconsumer products, such as facial tissue, bath tissue, paper toweling,paper napkins, and the like, as well as other non-paper-based consumerproducts. It has been found that softness is important to consumers inselecting and determining the quality and desirability of such products.Therefore, it is advantageous to be able to demonstrate the softness ofsuch a consumer product to the consumer, as a way of making the productmore desirable.

One method for quantifying softness has been to determine metrics thatdescribe fibers that emanate from the surface of a web substrate. Whilethe configuration of fibers emanating from the surface of a websubstrate may exist in many forms (e.g., fiber ‘loops’ where both endsof a fiber are attached to the surface and the middle of the fiber isnot, ‘free fibers’ where one end of the fiber is attached to the surfaceand the distal end is not, or other configurations of ‘free fibers’where the central portion of the fiber is attached to the surface andboth ends are not attached, etc.) it can be advantageous to understandthe metrics of the so-called ‘free fibers.’ This understanding of ‘freefibers’ is generally directed to those fibers attached to the underlyingweb substrate at one end while the distal end or part of the fiber isremoved from the surface or fibers where a central portion of suchfibers are attached to the surface and one or both ends are not. Thesemetrics are sometimes known to those of skill in the art as the ‘freefiber end’ number or the ‘fuzz-on-edge’ value.

One method for determining the free fiber end number involves the manual(i.e., optical) counting of the number of free fibers whose one end isvisible and unattached to a substrate surface. While this subjectivemethod may be sufficient in certain circumstances, the overall freefiber end number can be affected by the person doing the counting (e.g.,random error, fatigue, etc.) as well as the need for value judgmentsbased upon what is believed to be contained within the image.Additionally, experience has shown that it can take between sixty andninety minutes to perform a single analysis using this manual method.While the method itself may produce reasonable data, it can be difficultto perform adequate quality assurance to verify the data generated.

Another method used to quantify free fibers involves estimating theratio between the length of the profile that outlines the free fibersand the width of the samples tested to provide an average fuzz-on-edgevalue or amount of free fibers. Such a method is described in U.S. Pat.No. 6,585,855 B2.

A significant draw-back of the above-mentioned analyses are that theseprocesses can only provide one metric for the free fibers on a sample.These methods are difficult to adjust in order to provide othersample-related metrics. In other words, different tests have to becompleted using different testing techniques and possibly apparatii inorder to provide a more complete picture of the metrics associated witha particular sample or product.

Additionally, having a more dynamic method of demonstrating the softnessof a consumer product, using easily understood methods and familiar testmaterials, is clearly desirable. Compressibility and free fibers bothcontribute to product softness but are very different properties of thesubstrate. However a significant drawback of using the compressibilitymeasure to express softness is that the results of scientificcompressibility testing, while perhaps easily understood by one who isliterate in the art of materials testing or in mathematics, may not beunderstood by the average consumer in relation to the subjectiveperception of softness. An ideal method for demonstrating softness woulduse the consumer product in a manner easily understood and related to byconsumers. Such a method could be filmed or photographed and then usedin advertisements, or it could be carried out in the direct presence ofconsumers, as a live demonstration in a store or other public location.

SUMMARY OF THE INVENTION

An exemplary embodiment of the present disclosure provides a method forcounting the number of fibers emanating from the surface of a websubstrate. The web substrate has a machine direction, a cross-machinedirection orthogonal and coplanar thereto, and a Z-direction orthogonalto both the machine and cross-machine directions. The method comprisingthe steps of: a) providing an image file of the web substrate, the imagefile containing at least a two-dimensional image of the web substratewherein at least one of the at least two-dimensions comprises at least acomponent of the Z-direction; b) establishing a Z-direction baselinewith respect to the image file having a length, L, and being generallyco-planar to the Z-direction and having a component orthogonal to theZ-direction; c) determining a pixel intensity of a first pixel disposedabove the Z-direction baseline a height, d, and along the length L; d)determining a pixel intensity of a second pixel disposed the height, d,disposed adjacent the first pixel, the second pixel being disposed theheight, d, above the Z-direction baseline and generally orthogonal tothe Z-direction; e) determining a change of intensity between the firstpixel and the second pixel; f) determining a number of changes in thechange of intensity from step e); and, g) correlating the number ofpositive changes from the step f) to the number of fibers emanating fromthe surface of the web substrate.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary rendering of an apparatus suitable for generatingan image file suitable for use with the current invention;

FIG. 2 is an exemplary rendering of a frame and removable holdersuitable for holding a product such as a web substrate suitable for usewith the current invention;

FIG. 3 is a photomicrograph of a perspective view of an exemplary standsuitable for holding an exemplary holder suitable for use with thecurrent invention;

FIG. 4 is a photomicrograph of a perspective view of an exemplary standwith an exemplary holder suitable for use with the current invention;

FIG. 5 is a photomicrograph of a perspective view of an exemplary standwith an exemplary holder having an exemplary tissue product containedtherein in accordance with the current invention in the process of beingprepared for imaging;

FIG. 6 is a photomicrograph of a perspective view of an exemplary holderhaving an exemplary tissue product contained therein in accordance withthe current invention;

FIG. 7 is a photomicrograph of an exemplary tissue product showing freefibers emanating from a surface thereof;

FIG. 8 is a photomicrograph of an exemplary tissue product showing freefibers emanating from a surface thereof with a region of interest (ROI)selected;

FIG. 9 is a photomicrograph of an exemplary tissue product showing freefibers emanating from a surface thereof with a region of interest (ROI)selected and a baseline filtered using an exemplary low pass butterfilter, having an exemplary cut-off frequency of 30 Hz and an order of5, determined;

FIG. 10 is a photomicrograph of an exemplary tissue product showing freefibers emanating from a surface thereof with a region of interest (ROI)selected and an overall profile filtered using an exemplary low passbutter filter, having an exemplary cut-off frequency of 30 Hz and anorder of 5, determined;

FIG. 11 is a photomicrograph of an exemplary tissue product showing freefibers emanating from a surface thereof with a region of interest (ROI)selected suitable for determining the area enclosed between the desiredline profiles filtered using an exemplary low pass butter filter havingan exemplary cut-off frequency of 30 Hz and an order of 5;

FIG. 12 is a photomicrograph of an exemplary tissue product showing freefibers emanating from a surface thereof with a region of interest (ROI)selected suitable for determining the number of free fibers counted atsuccessive line profiles with a fixed inter-layer distance (ILD) betweenthem; and,

FIG. 13 is a graphical representation of the number of free fibersdetermined at successive line profiles with a fixed inter-layer distance(ILD) between them.

DETAILED DESCRIPTION OF THE INVENTION

As used herein, “image file formats” (or “image files”) are standardizedmeans of organizing and storing digital images. Image files are composedof either pixels, vector (geometric) data, or a combination of the two.Whatever the format, the files are rasterized to pixels when displayedon most graphic displays. The pixels that constitute an image areordered as a grid (columns and rows); each pixel consists of numbersrepresenting magnitudes of intensity and color.

Image file size—expressed as the number of bytes—increases with thenumber of pixels composing an image, and the color depth of the pixels.The greater the number of rows and columns, the greater the imageresolution for a fixed field of view and the larger the image file.Image files can be provided as grey-scale image files, be oriented asmay be required by the end user, and be readily converted to other fileformats by processing.

High resolution cameras and scanners can produce large image files,ranging from hundreds of kilobytes to gigabytes, per the camera'sresolution and the image-storage format capacity. For example, an imagerecorded by a 12 megapixel camera; since each pixel uses 3 bytes torecord true color, the uncompressed image would occupy 36,000,000 bytesof memory—a great amount of digital storage for one image, given thatcameras must record and store many images to be practical. Faced withlarge file sizes, both within the camera and a storage disc, image fileformats were developed to store such large images. An overview of themajor graphic file formats some of which use compression to reduce filesize follows below.

Including proprietary types, there are hundreds of image file types. ThePNG, JPEG, TIFF, and GIF formats are most often used to display images.These graphic formats can be separated into two main families ofgraphics: raster and vector.

In addition to straight image formats, metafile formats are portableintermediate formats which can include both raster and vectorinformation. Examples are application-independent formats such as WMFand EMF. Several known applications open metafiles and then save them intheir own native format. Another format, the page description language(PDL) describes the layout of a printed page containing text, objectsand images in textual or binary data streams. Examples includePostScript, PDF and PCL.

As used herein, a “gray scale” or “grey scale” digital image is an imagein which the value of each pixel is a single sample, that is, it carriesonly intensity information These images are composed exclusively ofshades of gray, varying from black at the weakest intensity to white atthe strongest. Gray scale images are distinct from one-bit bi-tonalblack-and-white images, which in the context of computer imaging areimages with only the two colors, black, and white (also called bi-levelor binary images). Gray scale images are often the result of measuringthe intensity of light at each pixel in a single band of theelectromagnetic spectrum (e.g. infrared, visible light, ultraviolet,etc.), and in such cases they are monochromatic proper when only a givenfrequency is captured. But also they can be synthesized from a fullcolor image; see the section about converting to gray scale.

For gray scale images the intensity of a pixel is expressed within agiven range between a minimum and a maximum, inclusive. This range isrepresented in an abstract way as a range from 0 (total absence, black)and 1 (total presence, white), with any fractional values in between.This notation is used in academic papers, but it must be noted that thisdoes not define what “black” or “white” is in terms of colorimetry.Another convention is to employ percentages, so the scale is then from0% to 100%. This is used for a more intuitive approach, but if onlyinteger values are used, the range encompasses a total of only 101intensities, which are insufficient to represent a broad gradient ofgrays. In computing, although the gray scale can be computed throughrational numbers, image pixels are stored in binary, quantized form.Some early gray scale monitors can only show up to sixteen (4-bit)different shades, but today gray scale images (as photographs) intendedfor visual display (both on screen and printed) are commonly stored with8 bits per sampled pixel, which allows 256 different intensities (i.e.,shades of gray) to be recorded, typically on a non-linear scale. Theprecision provided by this format is barely sufficient to avoid visiblebanding artifacts, but very convenient for programming due to the factthat a single pixel then occupies a single byte.

Technical uses (e.g. in medical imaging or remote sensing applications)often require more levels, to make full use of the sensor accuracy(typically 10 or 12 bits per sample) and to guard against round-offerrors in computations. Sixteen bits per sample (65,536 levels) is aconvenient choice for such uses, as computers manage 16-bit wordsefficiently. The TIFF and the PNG (among other) image file formatsgenerally support 16-bit gray scale natively, although browsers and manyimaging programs tend to ignore the low order 8 bits of each pixel. Inany regard, no matter what pixel depth is used, the binaryrepresentations one of skill in the art will presume that 0 is black andthe maximum value (255 at 8 bpp, 65,535 at 16 bpp, etc.) is white, ifnot otherwise noted.

Conversion of a color image to gray scale is not unique; differentweighting of the color channels effectively represent the effect ofshooting black-and-white film with different-colored photographicfilters on the camera and/or scanner. A common strategy is to match theluminance of the gray scale image to the luminance of the color image.

To convert any color to a gray scale representation of its luminance,first one must obtain the values of its red, green, and blue (RGB)primaries in linear intensity encoding, by gamma expansion. Then, addtogether 30% of the red value, 59% of the green value, and 11% of theblue value (these weights depend on the exact choice of the RGBprimaries, but are typical). Regardless of the scale employed (0.0 to1.0, 0 to 255, 0% to 100%, etc.), the resultant number is the desiredlinear luminance value; it typically needs to be gamma compressed to getback to a conventional gray scale representation.

As used herein, a “binary image” is a digital image that has only twopossible values for each pixel. Typically the two colors used for abinary image are black and white though any two colors can be used. Thecolor used for the object(s) in the image is the foreground color whilethe rest of the image is the background color. In the document scanningindustry this is often referred to as bi-tonal.

Binary images are also called bi-level or two-level. This means thateach pixel is stored as a single bit (0 or 1). The namesblack-and-white, B&W, monochrome or monochromatic are often used forthis concept, but may also designate any images that have only onesample per pixel, such as gray scale images. In Photoshop parlance, abinary image is the same as an image in “Bitmap” mode.

Binary images often arise in digital image processing as masks or as theresult of certain operations such as segmentation, thresholding, anddithering. A binary image is usually stored in memory as a bitmap, apacked array of bits. A 640×480 image can require 37.5 KB of storage.Because of the small size of the image files, fax machines and documentmanagement solutions usually use this format.

“Fibrous structure,” as used herein, means an arrangement of fibersproduced in any papermaking machine known in the art to create a ply ofpaper product or absorbent paper product. Other materials are alsointended to be within the scope of the present invention as long as theydo not interfere or counter act any advantage presented by the instantinvention. Suitable materials may include foils, polymer sheets, cloth,wovens or nonwovens, paper, cellulose fiber sheets, co-extrusions,laminates, high internal phase emulsion foam materials, and combinationsthereof. The properties of a selected deformable material can include,though are not restricted to, combinations or degrees of being: porous,non-porous, microporous, gas or liquid permeable, non-permeable,hydrophilic, hydrophobic, hydroscopic, oleophilic, oleophobic, highcritical surface tension, low critical surface tension, surfacepre-textured, elastically yieldable, plastically yieldable, electricallyconductive, and electrically non-conductive. Such materials can behomogeneous or composition combinations.

The terms “multi-layered tissue paper web, multi-layered paper web,multi-layered web, multi-layered paper sheet, multi-ply tissue product,and multi-layered paper product” are all used interchangeably herein torefer to sheets of paper prepared from two or more layers of aqueouspaper making furnish which are preferably comprised of different fibertypes, the fibers typically being relatively long softwood andrelatively short hardwood fibers as used in tissue paper making. Thelayers are preferably formed from the deposition of separate streams ofdilute fiber slurries upon one or more endless foraminous surfaces. Ifthe individual layers are initially formed on separate foraminoussurfaces, the layers can be subsequently combined when wet to form amulti-layered tissue paper web. The plies of a multi-ply tissue productcan be substantially homogeneous in nature or they can be multi-layeredtissue paper webs.

As used herein, the term “single-ply tissue product” means that it iscomprised of one ply of creped or uncreped tissue; the ply can besubstantially homogeneous in nature or it can be a multi-layered tissuepaper web.

As used herein, the terms “tissue paper web, paper web, paper sheet andpaper product” are all used interchangeably to refer to sheets of papermade by a process comprising the steps of forming an aqueous papermakingfurnish, depositing this furnish on a foraminous surface, such as aFourdrinier wire, and removing the water from the furnish (e.g., bygravity or vacuum-assisted drainage), forming an embryonic web,transferring the embryonic web from the forming surface to a transfersurface traveling at a lower speed than the forming surface. The web isthen transferred to a fabric upon which it is through air dried to afinal dryness after which it is wound upon a reel.

The tissue paper of the present invention preferably has a basis weightranging from between about 5 g/m² and about 120 g/m², more preferablybetween about 10 g/m² and about 75 g/m², and even more preferablybetween about 10 g/m² and about 50 g/m². The soft tissue paper of thepresent invention preferably has a density ranging from between about0.01 g/cm³ and about 0.19 g/cm³, more preferably between about 0.02 g/m³and about 0.1 g/cm³, and even more preferably between about 0.03 g/cm³and about 0.08 g/cm³.

The tissue paper of the present invention further comprises papermakingfibers of both hardwood and softwood types wherein at least about 50% ofthe papermaking fibers are hardwood and at least about 10% are softwood.The hardwood and softwood fibers are most preferably isolated byrelegating each to separate layers wherein the tissue comprises an innerlayer and at least one outer layer.

The tissue paper product of the present invention is preferably creped,i.e., produced on a papermaking machine culminating with a Yankee dryerto which a partially dried papermaking web is adhered and upon which itis dried and from which it is removed by the action of a flexiblecreping blade.

Creping is a means of mechanically compacting paper in the machinedirection. The result is an increase in basis weight (mass per unitarea) as well as dramatic changes in many physical properties,particularly when measured in the machine direction. Creping isgenerally accomplished with a flexible blade, a so-called doctor blade,against a Yankee dryer in an on machine operation. A Yankee dryer is alarge diameter, generally 8-20 foot drum which is designed to bepressurized with steam to provide a hot surface for completing thedrying of papermaking webs at the end of the papermaking process. Thepaper web which is first formed on a foraminous forming carrier, such asa Fourdrinier wire, where it is freed of the copious water needed todisperse the fibrous slurry is generally transferred to a felt or fabricin a so-called press section where de-watering is continued either bymechanically compacting the paper or by some other de-watering methodsuch as through-drying with hot air, before finally being transferred inthe semi-dry condition to the surface of the Yankee for the drying to becompleted. While the characteristics of the creped paper webs,particularly when the creping process is preceded by methods of patterndensification, are preferred for practicing the present invention,un-creped tissue paper is also a satisfactory substitute and thepractice of the present invention using un-creped tissue paper isspecifically incorporated within the scope of the present invention.Un-creped tissue paper, a term as used herein, refers to tissue paperwhich is non-compressively dried, most preferably by through-drying.Resultant through air dried webs are pattern densified such that zonesof relatively high density are dispersed within a high bulk field,including pattern densified tissue wherein zones of relatively highdensity are continuous and the high bulk field is discrete.

To produce un-creped tissue paper webs, an embryonic web is transferredfrom the foraminous forming carrier upon which it is laid, to a slowermoving, high fiber support transfer fabric carrier. The web is thentransferred to a drying fabric upon which it is dried to a finaldryness. Such webs can offer some advantages in surface smoothnesscompared to creped paper webs.

Tissue paper webs are generally comprised essentially of papermakingfibers. Small amounts of chemical functional agents such as wet strengthor dry strength binders, retention aids, surfactants, size, chemicalsofteners, crepe facilitating compositions are frequently included butthese are typically only used in minor amounts. The papermaking fibersmost frequently used in tissue papers are virgin chemical wood pulps.Additionally, filler materials may also be incorporated into the tissuepapers of the present invention.

As used herein a “user unit” is utilized for the web substrates subjectto the respective test method. As would be known to those of skill inthe art, bath tissue and paper toweling are typically provided in aperforated roll format where the perforations are capable of separatingthe tissue or towel product into individual units. A “user unit” is theexpected least amount of finished product unit that a consumer wouldutilize in the normal course of product use. In this way, a single-,double-, or even triple-ply finished product that a consumer wouldnormally use would have a value of one user unit. For example, a common,perforated bath tissue or paper towel having a single-ply constructionwould have a value of 1 user unit between adjacent perforations.Similarly, a single-ply bath tissue disposed between three adjacentperforations (each across any one entire dimension of the product) wouldhave a value of 2 user units. Likewise, any two-ply finished productthat a consumer would normally use and is disposed between adjacentperforations would have a value of one user unit. For purposes of facialtissues that are not normally provided in a roll format, but as astacked plurality of discreet tissues, a facial tissue having one plywould have a value of 1 user unit. An individual two-ply facial tissueproduct would have a value of one user unit, etc.

“Web materials” or ‘web substrates” as used herein include productssuitable for the manufacture of articles upon which indicia may beimprinted thereon and substantially affixed thereto. Web materialssuitable for use and within the intended disclosure include fibrousstructures, absorbent paper products, and/or products containing fibers.Other materials are also intended to be within the scope of the presentinvention as long as they do not interfere or counter act any advantagepresented by the instant invention. Suitable web materials may includefoils, polymer sheets, cloth, wovens or nonwovens, paper, cellulosefiber sheets, co-extrusions, laminates, high internal phase emulsionfoam materials, and combinations thereof. The properties of a selecteddeformable material can include, though are not restricted to,combinations or degrees of being: porous, non-porous, microporous, gasor liquid permeable, non-permeable, hydrophilic, hydrophobic,hydroscopic, oleophilic, oleophobic, high critical surface tension, lowcritical surface tension, surface pre-textured, elastically yieldable,plastically yieldable, electrically conductive, and electricallynon-conductive. Such materials can be homogeneous or compositioncombinations.

Web materials also include products suitable for use as packagingmaterials. This may include, but not be limited to, polyethylene films,polypropylene films, liner board, paperboard, cartoning materials, andthe like. Additionally, web materials may include absorbent articles(e.g., diapers and catamenial devices). In the context of absorbentarticles in the form of diapers, printed web materials may be used toproduce components such as back sheets, top sheets, landing zones,fasteners, ears, side panels, absorbent cores, and acquisition layers.Descriptions of absorbent articles and components thereof can be foundin U.S. Pat. Nos. 5,569,234; 5,702,551; 5,643,588; 5,674,216; 5,897,545;and 6,120,489; and U.S. Patent Publication Nos. 2010/0300309 and2010/0089264.

As used herein, one of skill in the art will recognize that the machinedirection (MD) is the plane associated with the direction of travel of aweb substrate through any processing (e.g., converting, printing, etc.)equipment. One of skill in the art will recognize that the cross-machinedirection (CD) is the direction coplanar and orthogonal thereto. One ofskill in the art will recognize that the Z-direction is orthogonal toboth the CD and MD.

The process of the present invention provides generally, a method forthe determination of various product attributes resulting from theanalysis of the pixels of image files and/or gray scale images of thoseproducts. While the processes and products described herein aregenerally directed toward web substrates that have been subjected tovarious photographic and scanning techniques that result in theproduction of gray scale image files, one of skill in the art willreadily recognize that the analytical methods provided herein can alsobe adapted for use with full color scan image files as well as any otherfile type that provides some form of color differentiation betweenadjacent pixels of the scanned image.

Similarly, the products analyzed herein are only exemplary embodiments.It would be readily recognized by one of skill in the art that virtuallyany product that can be scanned or photographed and the resulting toproduce and image file can be so analyzed for the desired attributesought. In other words, the examples and techniques provided herein aremerely exemplary and non-limiting and should not be consideredexclusively limited.

EXAMPLES

An apparatus and method for quantifying the number of fibers emanatingfrom a surface (also used interchangeably with “free fiber measurementsystem” and “free fiber measurement” respectively herein) as well as theeffective height of fibers emanating from a surface (also usedinterchangeably with “effective fiber height” herein) can utilize animage gathering apparatus to configure a web substrate such as a facialtissue, bath tissue, paper toweling, paper napkins, as well as othersubstrates on a suitable image scanner in order generate an image file.The image gathering apparatus is preferably capable of providing ascanned image of the web substrate. The method described herein can thenuse software to measure the number of free fibers emanating from thesurface along a length of tissue and the average effective free fiberheight from the recorded image(s). The free fiber measurement systemgenerally includes a testing apparatus, an imaging system, andcomputer-based image analysis software.

Test Apparatus

Referring to FIG. 1, an exemplary and non-limiting image gatheringapparatus 10 suitable for use to create an image of the fibers extendingfrom the surface of a web substrate 12 (i.e., Z-direction fibers) alongthe length and/or width of a web substrate 12 can generally comprise thefollowing equipment:

-   -   (1) Image scanner 14—one of skill in the art will recognize that        virtually any image scanner 14 capable of creating an image file        suitable for the method of the present invention is suitable for        the purposes of the present invention. For purposes of this        disclosure, an exemplary but non-limiting suitable image scanner        14 is an Epson Perfection V 700 Photo. It is preferred that any        scanner selected can provide an image with a resolution of at        least about 50 dpi, or at least about 300 dpi, or at least about        1200 dpi, or at least about 9600 dpi. The flat bed desktop        digital image scanner 14 mentioned herein can be provided with        the following specifications:        -   Document Type Reflective        -   Document Source: Document Table        -   Auto Exposure Type: Photo        -   Image Type: 16-bit Gray scale        -   Resolution: 2400 dpi        -   Adjustments: Unsharp Mask (ON, Level=High)            -   Dust removal (On, Level=High)    -   Further details of the image scanner 14 are discussed infra.    -   (2) Sample holder 22—one of skill in the art will recognize that        sample holder 22 is preferably used to position a suitably        prepared web substrate 12 on the image scanner bed 18. The        exemplary sample holder 22 preferably positions the web        substrate 12 upon the image scanner bed 18 in order to        facilitate the image scanner 14 creating an image of fibers        extending from the web substrate 12 in the Z-direction. Further        details of the sample holder 22 are discussed infra.    -   (3) A reflection minimizing insert 20—further details of the        reflection minimizing insert 20 are discussed infra.

It should be realized by one of skill in the art that each component ofthe sample holder 22 can be made with any suitable material. It isfurther preferred that each component is constructed from materials madeusing Fused Deposition Modeling (FDM) technology

The sample holder 22 is generally formed from two portions: the sampleholder frame 16 and the substrate holder 24. The sample holder frame 16is preferably designed to permit the precise and repeatable placement ofthe sample holder 22 on the image scanner bed 18 of the image scanner14. The sample holder frame 16, preferably removable, attaches to theimage scanner bed 18. One of skill in the art could provide suchreleasable attachment by the placement of notches, detents, guides, andthe like positioned upon the image scanner bed 18 or the image scanner14.

The substrate holder 24 is generally configured to provide the websubstrate 12 with suitable and/or adequate tension. It was also foundthat the substrate holder 24 can also position the web substrate 12 intoa fixed position within the sample holder 22 and the resulting substrateholder 22 positioned relative to the image scanner bed 18 in aconsistent manner to facilitate imaging of the fibers extending from theweb substrate 12 in the Z-direction along the length of the websubstrate 12.

Referring to FIG. 2, the sample holder frame 16 preferably and generallycomprises two press-fit latches 26 that are used to secure the websubstrate 12 once it has been looped over a shim 28. By way ofnon-limiting example, shim 28 can be provided as a thin metal bar. Asuitable shim 28 for use with a single user unit thickness of bathtissue and facial tissue, independent of the number of plies, was foundto have a thickness of about 0.064 cm.

Referring again to FIG. 1, reflection minimizing insert 20 can bedesigned to minimize any background reflection from the image scanner 14glass top caused by the scanner light and can also provide a contrastingbackground to assist in the analysis of the web substrate 12. In apreferred embodiment, the reflection minimizing insert 20 is formed by aprocess utilizing fused deposition modeling (FDM) and is attached to thenotches typically found on top section of the chosen scanner. It shouldbe readily realized that the reflection minimizing insert 20 can bedesigned and formed using any process available. One of skill in the artwill understand that it would be advantageous to provide the reflectionminimizing insert 20 as a black felt material. Additionally, one ofskill in the art will recognize that reflection minimizing insert 20maybe attached or provided as unattached to the top section of thescanner. For example, the reflection minimizing insert 20 can be placeddirectly onto the sample holder frame 16 before or after the sampleholder frame 16 is placed in position for scanning by image scanner 14.

Experimental Protocol

For the exemplary method described herein, each sample of web substrate12 is prepared for testing according to the following process:

The web substrate 12 to be tested (by way of non-limiting example, bathtissue) is preferably cut to a length of at least 20 cm, its width beingequal to the standard user unit of the web substrate 12. The sample ispreferably conditioned at normal room temperature (e.g., 30° C.±1° C.)and humidity (e.g., ˜40%±5%) for at least 2 hours.

The sample of web substrate 12 is placed on the sample holder frame 16such that it loops over the shim 28 in either the MD or CD of the websubstrate 12. The region over the shim is preferably not the perforatedregion of the web substrate 12 (generally disposed in the CD) or an edgeof the web substrate 12 (generally in the MD) as these regions may notbe representative of the remainder of the sample that has not beensubjected to a mechanical cutting, slitting, and/or perforatingapparatus. For exemplary purposes only, the shim 28 is provided with thedimensions: length=10.6 cm, width=1.35 cm, and thickness 0.064 cm.Preferably, the web substrate 12 is positioned over shim 28 andpositioned within sample holder frame 16 so that the length of websubstrate 12 disposed on both sides of shim 28 are approximately equal.

As shown in FIGS. 3-5, sample holder frame 16 is then preferably affixedon stand 30. In this manner, it is believed that the web substrate 12can be subjected to an applied tension in an effort to reduce the angledisposed between web substrate 12 and shim 28. One of skill in the artwill recognize that reducing the overall angle disposed between websubstrate 12 and shim 28 can practically increase the ‘edge-like’qualities suitable for creating an image suitable for analysis of theweb substrate disposed over shim 28.

In order to present a more ‘edge-like’ appearance of the web substrate12 for analysis by the method described herein, it may be desirable into provide a tension to the web substrate 12 disposed over and aboutshim 28. One of skill in the art will recognize many methods to providesuch tension. However, one particularly useful solution was to affix aknown weight to the ends of the tissue sample disposed over shim 28. Oneof skill in the art will appreciate that such a known weight ispreferably affixed across the entire width of web substrate 22. For theanalysis described herein, a weight of 185 gm was found to providesuitable tension in a direction vertically downward (i.e., generallyparallel to the Earth's gravitational field) for bath tissue and facialtissue products. Naturally, one of skill in the art can provide tensionto the web substrate 12 disposed upon sample holder frame 16 in anyorientation—vertically downward, horizontally, or otherwise. In anyregard it is desired to provide sufficient tension to the ends of theweb substrate 12 draped over shim 28 in the MD, CD, or combinationthereof, in an effort to reduce the overall angle disposed between shim28 and the web substrate 12 draped overtherefrom. One of skill in theart will appreciate that the amount of weight affixed to the websubstrate 12 can be chosen based upon the known, or even presumed,physical characteristics of the web substrate 12 to be analyzed. By wayof non-limiting example, paper toweling may require a significant weightto be affixed in order to provide the desired edge-like appearance tothe web substrate 12. Thus, some factors to consider in selecting asuitable weight to affix to the web substrate 12 include, but are notlimited to, web substrate 12 basis weight, density, number of plies,flexural modulus, drape, combinations thereof, and the like.

Next the press-fit latches 26 are then pressed down to secure thetensioned sample in place. Any tensioning weight used is then removed.The resulting sample of web substrate 12 disposed within sample holderframe 16 is shown in an exemplary but non-limiting manner in FIG. 6. Thecombined sample holder frame 16 with sample is then placed into thesample holder 22 disposed upon the bed of the image scanner 14, and theimage scanner 14 top closed for imaging and generation of the imagefile. An exemplary but non-limiting image scanner 14 set-up is providedinfra. In a preferred embodiment, a calibration image corresponding tothe same region of interest is recorded (a preferred calibration scalecan be provided with graduated markings of 0.1 mm resolution) for eachweb substrate 12 to be analyzed.

It is preferred that prior to the generation of each image file, thatappropriate care is taken to clean the glass surface of the imagescanner 14 and all parts cooperatively associated thereto. Additionally,one of skill in the art will appreciate that appropriate care be takento refrain from impacting the web substrate 12 in order to provide thebest image possible of the web substrate 12.

Alternatively, web substrate 12 can be prepared for analysis in a mannerconsistent with the present disclosure by the use of microtoming. Inthis alternative exemplary but non-limiting embodiment, one face of auser unit of web substrate 12 sample can be embedded into an epoxy resinor wax block or cryogenically frozen. A sectioning instrument can thencut thin slices of the web substrate 12 sample in the MD, CD, or anycombination thereof, into sections. One of skill in the art will easilyrecognize that microtomy can be used to provide microtome sectionshaving thicknesses ranging between 0.05 and 100 μm. Exemplary microtomessuitable for use in providing samples of web substrate 12 suitable foruse with the present method can include sledge microtomes, rotarymicrotomes, cryomicrotomes, ultramicrotomes, vibrating microtomes, sawmicrotomes, laser microtomes, and the like. The sample can then bedirectly disposed upon the bed of the image scanner 14, and the imagescanner 14 top closed for imaging and generation of the image file.

For the exemplary method described herein, the generated image fileshould contain at least a two-dimensional image of a web substrate whereat least one dimension of the image file contains at least a componentof the web substrate in the Z-direction. For purposes of the exemplarymethod described herein, the generated image file will provide an imageof an edge of the web substrate whether the edge is produced by theapparatus discussed supra, microtoming, or by any other method known tothose of skill in the art for practicing the process described herein.Additionally, for purposes of this disclosure,

A. Image Analysis Program

While it is possible that a wide plurality of image processing systemscan be used to analyze the image file of web substrate 12, it was foundthat reasonable image processing can be achieved using readily availablemathematics software such as MATLAB. The bold font used below denotesstandard functions available within the MATLAB software. Exemplarycommented code developed for this analysis is provided in Section Einfra.

Referring to FIGS. 7-12, an exemplary, but non-limiting image analysisprogram/code is described by the following steps:

-   -   1. Referring to FIG. 7, the image file is loaded into MATLAB and        contrast is corrected using the standard imadjust.m function.        The width and height of the image is denoted by a component of        the MD or CD directions, and Z-direction, respectively.    -   2. Referring to FIG. 8, the graphic interface allows the user to        select a rectangular region of interest (ROI) having a width of        length, L, orthogonal to the Z-direction of the web substrate        shown in the image by clicking and dragging the mouse.    -   3. Referring to FIG. 9, the program preferably uses the standard        im2bw.m and edge.m function to convert the image in Step 1 of        this Section to a binary format and reduce the resultant to an        image with only an edge profile that represents where the pixel        intensity transitions from white to black. Exemplary, but        non-limiting specifications of the edge.m function are: edge        finding method=‘Canny’.    -   4. The position coordinates (x (width: a position along L), Z        (height)) of each pixel of the edge profile is identified by        measuring pixel intensity along Z (height of the image) for a        single line of pixels measured using the improfile.m function.        For a given x position, the coordinates of the last pixel along        Z with intensity greater than zero is recorded. By convention        and for non-limiting purposes, the top left corner of the image        represents the origin (0, 0).    -   5. The analysis in Step 4 of this Section is repeated across the        length, L, of the image selected in Step 3 of this Section to        create a matrix of pixel positions.    -   6. The edge profile is obtained from the matrix of pixel        positions created in Step 5 of this Section after interpolating        within the matrix using the interp1.m function to ensure that        every x position has an associated pixel across the width of the        image selected in Step 3 of this Section. For non-limiting        purposes, specifications of the interp1.m function are:        method=‘spline’ used in extrapolation for elements outside the        specified interval.    -   7. As shown in FIG. 9, the edge profile from Step 6 of this        Section is then filtered using a low pass butter filter with the        exemplary specifications of a cut-off frequency=100 Hz and        order=5 to create a Z-direction baseline.

A. Calibration

In one embodiment of the present invention length calibration can beaccomplished by determining the pixel to centimeter conversion factor.One of skill in the art will appreciate that this process involvesdetermining the number of pixels that make up the actual physicaldistance between two points using the getline.m function. Generally, oneof skill in the art can use a scale with graduated markings 0.01 cmapart. For non-limiting purposes, the size of the calibration image mustbe the same as that of the web substrate image analyzed.

B. Estimating the Average Effective Height of the Free Fibers

In this embodiment, an exemplary and non-limiting program uses thestandard imfilter.m and edge.m function to convert the image file to animage with a single line of pixels with intensity equal to one (white).

-   -   1. Specification of the imfilter.m function can be provided        preferably as a two dimensional filter (fspecial.m)=‘unsharp’.        Specifications of the edge.m function can be: edge finding        method=‘Canny’.    -   2. The function improfile.m is used to determine from the image        generated above the position coordinates of the first pixel        along Z (height of the image), the location of a pixel with        intensity equal to one.    -   3. The analysis performed in Step 2 in this Section is repeated        across the width of the ROI (length, L) identified in Step 2        from Section A above.    -   4. The edge profile obtained by creating a matrix with all the        pixel positions identified in Step 3 in this Section is        interpolated using the interp1.m function to ensure that the        profile is described for every x position across the width of        the image selected in Step 2 from Section A above.        Specifications of the interp1.m function are: method=‘spline’        used in extrapolation for elements outside the specified        interval.    -   5. The edge profile from Step 4 in this Section is then filtered        using a low pass butter filter having the exemplary        specifications: Cut-off frequency=100 Hz and order=5. All        Z-coordinate values along the edge profiles measured here with        values greater than the corresponding Z-direction baseline        estimated in Step 7 of Section A above are made equal to it.    -   6. The function trapz.m numerically integrates the area under        the edge profile identified in Step 5 in this Section.    -   7. The function trapz.m numerically integrates the area under        the Z-direction baseline identified in Step 7 in Section A        above.    -   8. The net area or area enclosed between the two profiles is        given by the magnitude of the difference in the absolute values        of the areas estimated in Steps 6 and 7 in this Section.    -   9. The net area from Step 8 in this Section divided by the width        of the ROI (length, L) gives the average effective height of the        free fibers in pixels.    -   10. Referring to FIG. 11, using the calibration constant        estimated in Section B the average effective height of the free        fibers can be converted to centimeters.

C. Estimating the Number of Free Fibers

-   -   1. Pixel intensities along an edge profile across the width of        the selected ROI in Step 3 from Section A above is recorded        using the improfile.m function. The Z-direction baseline        obtained in Step 7 from Section A above with the Z position of        each pixel offset by a fixed factor can be considered a line        profile.    -   2. The threshold intensity values for the web substrate image        are obtained by processing the intensity of pixels that exist        within the bounds described by the maximum Z coordinate of the        image and the maximum Z-coordinate of the ROI. A suitable        threshold may be developed by averaging the maximum in the        derivative of the intensity (after it has been filtered using a        low pass butter filter with the exemplary specifications of a        cut-off frequency=30Hz and order=1) along each line of pixels        orthogonal to the Z-direction (downwards) within the section of        the ROI described above.    -   3. As shown in FIG. 12, the pixel intensities of the line        profile are recorded as in Step 1 in this Section between the        following Z coordinate limits:        -   a. START: Offset a fixed distance in the Z-direction below            the Z-direction baseline identified in Step 7 in Section A            above. The fixed distance is two-thirds the distance between            the minimum Z values of the Z-direction baseline and ROI.        -   b. STOP: at a height in the image in where the mean height            of pixels in the line profile is greater than the height of            the ROI.    -   4. One of skill in the art can choose an ILD (inter-layer        distance) of 1 pixel but in the interest of computational time        it may be preferred to use an ILD value that is a function of        the Z-variation in the Z-direction baseline measured in Step 7        of Section A.    -   5. The intensities recorded for each line profile in Step 3 in        this Section can be smoothed using a moving average method.    -   6. For each line of pixel intensities processed in Step 5 in        this Section the first derivative of intensity is computed.        Peaks in the intensity derivative represent the transitions from        black to white or vice versa.    -   7. The intensity derivative calculated in Step 6 in this Section        is filtered using a low pass butter filter (exemplary and        non-limiting cut off frequency=100 Hz and order=5).    -   8. The extrema.m function is used to identify the peaks in each        profile conditioned in Step 7 in this Section. Exemplary, but        non-limiting peak identification function used like extrema.m        can be obtained at:        -   http://www.mathworks.com/matlabcentral/fileexchange/12275    -   9. The numbers of peaks identified in Step 8 in this Section        with intensity values greater than the threshold value (from        Step 2 in this Section) are counted.    -   10. Referring to FIG. 14, the number of free fibers can be        graphically presented. The number of free fibers can then be        approximated as a percentage of the maximum number of free fiber        in a layer that occurred above a fixed distance from the base        profile. It was surprisingly found that 90% and 0.1 mm distance        are values that provide consistent results however, it should be        understood that any percentage and distance values could be used        as provided herein with success.    -   11. Using the calibration constant from the Calibration section        (Section B) above, the number of free fibers per centimeter can        be estimated.

D. Exemplary MATLAB Program for Use in Estimating the Effective Heightof Free Fibers and Estimating the Number of Free Fibers in a WebSubstrate

The following code was found suitable for providing the above-describedanalysis and the ensuing calculation of the above-described metrics. Itshould be understood by one of skill in the art that the followingcommented code is completely exemplary and clearly non-limiting.

% The code below includes comments that are preceded by the ‘%’ sign

close all; clear all; clear mex; % CALIBRATING THE IMAGEnameimg_cal=′C:\DATA ANALYSIS\Curr_Bus\'; cal=input(′Input the filenamefor calibration:′,′s′);filenamebase_cal=strcat(nameimg_cal,num2str(cal),′.tif);mm_cal=imread(filenamebase_cal); figure(88); imshow(mm_cal);CALIBVAL=input(′Calibration length (in cm):′); % Input distance betweenthe markers hx hy]=getline;new_CAL=CALIBVAL/sqrt((hx(2,1)-hx(1,1)){circumflex over( )}2+(hy(2,1)-hy(1,1)){circumflex over ( )}2); % 1pixel = new_CAL cm %%DETERMINING THE AVERAGE EFFECTIVE HEIGHT OF THE FREE FIBERS %FILE SOURCEnameimg=′C:\DATA ANALYSIS\Curr_Bus\XX.tif′; rr=colormap(jet);mm=imread(nameimg); %Read in the image file mm_kg=imadjust((mm));figure(612); imshow(mm_kg) % Show the read image %imshow(mm_kg);title(′Original image with scale bar′); uiwait(msgbox(′********NOTE: Getcalibration image if ROI has been changed*******′,Title′,′modal′));%Request for calibration to be done %SELECTING ANALYSIS REGIONcrop_lim=getrect;  %xmin ymin width height ulim=crop_lim;xcrop=[crop_lim(1,1) crop_lim(1,3)+crop_lim(1,1)crop_lim(1,3)+crop_lim(1,1) crop_lim(1,1) crop_lim(1,1)];ycrop=[crop_lim(1,2) crop_lim(1,2) crop_lim(1,2)+crop_lim(1,4)crop_lim(1,2)+crop_lim(1,4) crop_lim(1,2)]; figure(61); hold on;plot(xcrop,ycrop,′y--′,′LineWidth′,2); figure(61); % FIBER EDGEDETECTION h=fspecial(′unsharp′); BWM=imfilter(mm_g,h); BW1 =edge(BWM,′canny′); %EDGE PROFILE DETECTION imshow(BW1); BWG=im2bw(mm_g);%Z-DIRECTION BASELINE DETECTION BW2 = edge(BWG,′canny′); figure(343)imshow(BW2) figure(454); subplot(2,1,1) imshow(BW1) %EDGE PROFILE IMAGEsubplot(2,1,2) imshow(BW2); %BASE PROFILE IMAGE %VARIABLES USEDtot_ggy=[ ]; tot_ggx=[ ]; over_gg=[ ]; tt=0; over_I=[ ]; over_pos=[ ];over_S=[ ]; over_Spos=[ ]; figure(61); imshow(mm_g);set(gcf,′color′,′white′); %Z-DIRECTION BASELINE AND EDGE PROFILEIDENTIFICATION  for ii=fix(ulim(1,1)):fix((ulim(1,1)+ulim(1,3)))   xx=[];   yy=[ ];   yy = fix(ulim(1,2)):(fix(ulim(1,2))+fix(ulim(1,4)));   xx= ii + zeros(1,fix(ulim(1,4))+1);   clear gg gg_x gg_y ss ss_x ss_y;  [gg_x,gg_y,gg] = improfile(BW1,xx,yy); %EDGE PROFILE   [ss_x,ss_y,ss]= improfile(BW2,xx,yy); %Z-DIRECTION BASELINE     S=find(ss >0,1,′last′); % IDENTIFY THE LAST PIXEL WITH INTENSITY > 0      ifulim(1,2)<S<(ulim(1,2)+ulim(1,4))      over_S=[over_S S+ulim(1,2)];     over_Spos=[over_Spos ii];      hold on;     plot(ii,S+ulim(1,2),′co′,′MarkerSize′,4); %Z-DIRECTION BASELINE    end     I=find(gg==1,1,′first′); % IDENTIFY THE FIRST PIXEL WITHINTENSITY = 1     if I==0      I=ulim(1,2);     end     if I>S      I=S;    end   over_I=[over_I I+ulim(1,2)]; over_pos=[over_pos ii]; hold on;plot(ii,I+ulim(1,2),′mo′,′MarkerSize′,4); %OVERALL PROFILEover_gg=[over_gg gg]; tot_ggx=[tot_ggx gg_x]; tot_ggy=[tot_ggy gg_y];hold on; end figure(63);clf; imshow(mm_g); %FILTERING/INTERPOLATING THEIDENTIFIED Z-DIRECTION BASELINE AND EDGE PROFILES  over_I(1,end)=mean(over_I);  over_S(1,end)=mean(over_S); gh=butterfilter(interp1(over_pos,over_I,ulim(1,1):(ulim(1,1)+ulim(1,3)),′spline′.′extrap′),100,5);%interpolated intensity locationssh=butterfilter(interp1(over_Spos,over_S,ulim(1,1):(ulim(1,1)+ulim(1,3)),′spline′,′extrap′),100,5); for bb=1:length(sh) %REMOVING ALL EDGE PROFILE ELEMENTS THAT ARE LESSTHAN THE CORRESPONDING Z DIRECTION BASELINE VALUES  if (gh(bb)-sh(bb)>0)  gh(bb)=sh(bb);  else   gh(bb)=gh(bb);  end  end  hold on; plot(ulim(1,1):(ulim(1,1)+ulim(1,3)),gh,′r.′,′MarkerSize′,6) plot(ulim(1,1):(ulim(1,1)+ulim(1,3)),sh,′b.′,′MarkerSize′,6) jbfill(ulim(1,1):(ulim(1,1)+ulim(1,3)),sh′,gh′,′y′) %EFFECTIVE HEIGHTESTIMATION A1 =trapz(ulim(1,1):(ulim(1,1)+ulim(1,3)),gh);A2=trapz(ulim(1,1):(ulim(1,1)+ulim(1,3)),sh); A=abs(A1-A2); %units arepixel{circumflex over ( )}2 Atot=A*new_CAL*new_CAL; %AEA AND ROI WIDTHCONVERTED TO cm USING THE CALIBRATION CONSTANTstrip_width=ulim(1,3)*new_CAL; Effective_height=Atot/strip_width; %%ESTIMATING THE NUMBER OF FREE FIBERS PER CM figure(61); imshow(mm_g)hold on plot(xcrop,ycrop,′y--′,′LineWidth′,2); [Cx,Cy,C] =improfile(mm_g,ulim(1,1):(ulim(1,1)+ulim(1,3)),sh);plot(Cx,Cy,′r--′,′LineWidth′,2); %FIXING #LAYERS AND INTER-LAYERDISTANCE (ILD) kk=0; tl=1; % Inter-layer distance (ILD) set to 1crop_mm=mm_g; start_pt=fix(2*(max(ycrop)-mean(Cy))/3); %START POINT FORTHE ANALYSIS % Variables ii=0; x1=[ ]; y1=[ ]; over_gg=[ ];over_gg_smt=[ ]; tot_yy=[ ]; gg_smt=[ ]; ii=kk; % THRESHOLD VALUES FORTHE FOREGROUND AND BACKGROUND figure(64); imshow(mm_g) title(′Gettingthe foreground/background threshold values′); hold onplot(xcrop,ycrop,′y--′,′LineWidth′,2); plot(Cx,Cy,′r.′,′LineWidth′,2);lj=size(mm_g); tot_thresh=[ ]; max_hh=[ ]; forzz=0:(ulim(1,2)+ulim(1,4)-max(Cy))  [hh_x,hh_y,hh] =improfile(mm_g,ulim(1,1):(ulim(1,1)+ulim(1,3)),ones(length(ulim(1,1):(ulim(1,1)+ulim(1,3))),1)*(max(Cy)+zz)); figure(64); hold on; plot(hh_x,hh_y,′g.′); tot_thresh=[tot_hresh max(butterfilter(diff(hh),30,1))]; endthresh=mean(tot_thresh); figure(64); if ulim(1,2)-ulim(1,4)<0 zz_up=ulim(1,2); else  zz_up=ulim(1,4); end max_gg=[ ]; tot_gg=[ ]; forzz=0:zz_up-1 [gg_x,gg_y,gg],improfile(mm_g,ulim(1,1):(ulim(1,1)+ulim(1,3)),ones(length(ulim(1,1):(ulim(1,1)+ulim(1,3))),1)*(ulim(1,2)-zz)); figure(64); hold on; plot(gg_x,gg_y,′c.′); tot_gg=[tot_gg max(butterfilter(diff(gg),30,1))]; end bkg_val=mean(tot_gg);ds=100; gg=[4000]; %initializing gg %IDENTIFYING THE #LAYERS while((max(Cy(1:end,1)-(tl*ii)+start_pt)> min(ycrop))) % STOP COUNTING THENUMBER OF FREE FIBERS WHEN LINE PROFILE GOES OUT OF THE ROI  xx=[ ]; yy=[ ];  %gg=[ ];  kk=kk+1; %counts number of layers   ii=kk;  xx =[Cx(1:end,1)];  yy = [Cy(1:end,1)-(tl*ii)+start_pt]; % add offset to thestart point of analysis  [gg_x,gg_y,gg] = improfile(crop_mm,xx,yy); x1=[x1 xx];  y1=[y1 yy];   tt=size (gg);   R=rem(kk,5);  if(R==0)  %ii=kk;    figure(610);    %imshow(crop_mm);   %plot(1:tt(1,1),gg,′Color′,[fix(rr(fix(ii),1)*64/ds)fix(rr(fix(ii),2)*64/ds) fix(rr(fix(ii),3)*64/ds)]);   plot(1:tt(1,1),gg,′Color′,′y′);    %plot(xx,gg,′c′);    xlabel(′xposition (pix)′);    %ylabel(′pixel intensity′);  figure(61); %plot(gg_x,gg_y,′Color′,[fix(rr(fix(ii),1)*64/ds)fix(rr(fix(ii),2)*64/ds) fix(rr(fix(ii),3)*64/ds)],′LineWidth′,1); plot(gg_x,gg_y,′Color′,y,′LineWidth′,1);  end  over_gg=[over_gg gg]; tot_yy=[tot_yy Cy(1,1)-(tl*ii)];  hold on; end figure(61); zoom off;title(strcat(′Number of layers:′,num2str(kk),′ Layer thickness (pix):′,num2str(tl))); % SMOOTHING THE INTENSITY PROFILE for jj=1:kk Sze_gg=size(over_gg(:,jj));  %%% jj=1;  for ii = 3:Sze_gg(1,1)-2  gg_smt(ii,jj)=(over_gg(ii-2,jj)+2*over_gg(ii-1,jj)+3*over_gg(ii,jj)+2*over_gg(ii+1,jj)+over_gg(ii+2,jj))/9;  end    figure(68);clf;set(gcf,′color′,′white′);   plot(over_gg(:,jj),′r′,′LineWidth′,2);    hold on   plot(gg_smt(:,jj),′b-′,′LineWidth′,1);    ylabel(′Pixel intensity′);   xlabel(′x position of pixel′);    title(strcat(′Smoothing out theintensity data-layer number: ′,num2str(jj))); end % ESTIMATING/COUNTINGINTENSITY PEAKS/FIBERS %Variables tot_dd=[ ]; det_gg=[ ]; tot_dd=[ ];figure(67); det_gg =diff(gg_smt(:,kk)); %kk=4; for ii=1 :kk  dd=0; det_gg(:,ii) = diff(gg_smt(:,ii));  figure(65);  axis(0 5000 −20002000]);  plot(det_gg(:,ii),′Color′,′k′);  hold on; set(gcf,′color′,′white′);  xlabel(′index′);  ylabel(′derivative ofintensity′);  clear filt_det num_det num_det=find(extrema(smooth(butterfilter(det_gg(:,ii),100,1),7))>thresh);% Picking peaks in intensity derivative  dd=length(num_det); %we includethe −1 to account for the initial pixel transition  if dd % REMOVEPOSSIBLITY OF NEGATIVE NUMBER OF FIBERS   dd=0;  else   dd=dd;  end figure(67);  plot(ii,dd,′{circumflex over( )}′,′Color′,′k′,′MarkerSize′,6,′LineWidth′,2,′MarkerFaceColor′,′g′); hold on  tot_dd=[tot_dd dd]; end figure(67); hold on;plot(ones(1,length(1:max(tot_dd))).*fix(start_pt/tl),1:max(tot_dd);′k.′); set(gcf,′color′,′white′);xlabel(′layers′); ylabel(′Number of fibers in ROI′); %IDENTIFYING THELAYER CORRESPONDING TO THE 0.01cm CONDITION count_layer =fix(0.01/(new_CAL*d));figure(67); hold on;plot(ones(1,length(1:max(tot_dd))).*fix(start_pt/tl+count_layer),1:max(tot_dd),′ro′);plot(fix(start_pt/tl+count_layer),fix(max(tot_dd(fix(start_pt/t1+count_layer):end))*0.9),′ys′,′MarkerSize′,12,′MarkerFaceColor′,′r′);plot(fix(start_pt/tl+count_layer)+1,fix(max(tot_dd(fix(start_pt/tl+count_layer)+1:end))*0.9),′yo′,′MarkerSize′,12,′MarkerFaceColor′,′b′); figure(61);plot(Cx,Cy-(count_layer*tl),′c--′,′LineWidth′,1); % ESTIMATING THENUMBER OF FREE FIBERS Number_of_free_fibers_per_unit_length=fix((max(tot_dd(fix(start_pt/tl+count_layer)+1:end))/strip_width)*0.9);%90% the maximum is taken as peak number of fibersBEFNumber_of_free_fibers_per_unit_length=fix((max(tot_dd(fix(start_pt/tl+count_layer):end))/strip_width)*0.9);%90% the maximum is taken as peak number of fibers

It was found that the process of the present invention can also be usedto quantify other useful metrics (e.g., physical parameters) for variousweb substrates having fibers, filaments, threads, and the like extendingfrom a surface thereof. This can include, but is not limited to:

-   -   Number of Free Fiber (FP) spatial distribution        -   From the image obtained using the above method we can            determine the spatial location of each free fiber counted            (using the above method) or divide the width of the tissue            into bins and determine the number of free fibers in each            bin. A mathematical technique like Fourier analysis can then            be used to compute the spatial distribution of the number of            free fibers.    -   Average Effective FP height spatial distribution        -   From the image obtained using the above method we can divide            the width of the tissue into bins and determine the average            effective height for each bin. A mathematical technique like            Fourier analysis can then be used to compute the spatial            distribution of the average effective FP heights.    -   Distribution of average effective heights        -   From the image obtained using the above method we can divide            the average effective height of fibers on a tissue into bins            to obtain the distribution.    -   Free Fiber Area        -   The total number of pixels with intensity greater than or            equal to the threshold within the regions described by Steps            8 in Section A, and 5 in Section C (steps that correlate to            overall and base profiles of the substrate) would correspond            to the FF Area.    -   Width of the FP        -   The width of a fiber may be determined by obtaining the            derivative of the intensity of the pixels along the width of            a sample a fixed distance from and parallel to the base            profile. The distance between intensity transitions of the            opposite sign would correlate with the width of the fiber.    -   Distribution of PP widths        -   From the image obtained using the above method we can            estimate the width of each FF as described above and obtain            the distribution of the different widths measured.    -   Identifying different color fibers in a multi-colored web        substrate and determine associated metrics        -   The image data for a multi-colored web substrate can be            collected using a similar setup and protocol but the image            should be collected in color (ex. 24-bit). Using a suitable            calibration with a standard color sampler thresholds can be            determined that correspond to specific colors. The number of            FP corresponding to each color can then be estimated using            these thresholds for the web substrate with an analysis            similar to that presented in this invention.

Additionally, the method of analysis used herein can be used for varioushousehold, cosmetic, and personal implements having the need forexemplary products such as:

-   -   Estimating the number of bristles on an object (toothbrush or        mascara applicator)        -   For such an application an image for analysis may be            obtained by inserting a contrasting background oriented            generally parallel to the bristles and orienting the object            generally orthogonal to the imaging device. As an            alternative, to remove the effect of interacting bristles            the object may also be micro-machined to a relevant            thickness before being imaged against a contrasting            background.    -   Estimating the loss in color intensity and/or number of fibers        disposed upon fabrics (e.g., color retention type problems)        -   This would use our method in the same form the issue could            be because of the fact that cotton has multiple smaller            fibers that wrap together to form a single fiber (from            yarn). This might need addition filtering or tweaking at the            image analysis stage. To overcome the issues with thickness            for certain fabrics the fabric may be machined into thin            strips along the length or width, laid falt on a contrasting            background and imaged.    -   Household cleaning implements (e.g., Swiffer pads)        -   To overcome the issues with thickness for certain fabrics            the fabric may be machined into thin strips along the length            or width, laid flat against a contrasting background and            imaged.    -   Facial hair (e.g., razor development) or Eyelash measurement.

The image analysis approach for each of the above described applicationswould remain similar to that described in this invention. The mainsource of variation would be in the obtaining of the image and thepreparation of the sample.

The dimensions and values disclosed herein are not to be understood asbeing strictly limited to the exact numerical values recited. Instead,unless otherwise specified, each such dimension is intended to mean boththe recited value and a functionally equivalent range surrounding thatvalue. For example, a dimension disclosed as “40 mm” is intended to mean“about 40 mm.”

All documents cited in the Detailed Description of the Invention are, inrelevant part, incorporated herein by reference; the citation of anydocument is not to be construed as an admission that it is prior artwith respect to the present invention. To the extent that any meaning ordefinition of a term in this document conflicts with any meaning ordefinition of the same term in a document incorporated by reference, themeaning or definition assigned to that term in this document shallgovern.

While particular embodiments of the present invention have beenillustrated and described, it would be obvious to those skilled in theart that various other changes and modifications can be made withoutdeparting from the spirit and scope of the invention. It is thereforeintended to cover in the appended claims all such changes andmodifications that are within the scope of this invention.

What is claimed is:
 1. A method for counting the number of fibersemanating from the surface of a web substrate, the web substrate havinga machine direction, MD, a cross-machine direction, CD, orthogonal andcoplanar thereto, and a Z-direction orthogonal to both said machine andcross-machine directions, said method comprising the steps of: a)providing an image file of said web substrate, said image filecontaining at least a two-dimensional image of said web substratewherein at least one of said at least two-dimensions comprises at leasta component of said Z-direction; b) establishing a Z-direction baselinewith respect to said image file having a length L and being generallyco-planar to said Z-direction and having a component orthogonal to saidZ-direction; c) determining a pixel intensity of a first pixel disposedabove said Z-direction baseline a height, d, and along said length L; d)determining a pixel intensity of a second pixel disposed said height, d,disposed adjacent said first pixel, said second pixel being disposedsaid height, d, above said Z-direction baseline and generally orthogonalto said Z-direction; e) determining a change of intensity between saidfirst pixel and said second pixel; f) determining a number of positivechanges in said change of intensity from step e); and, g) correlatingsaid number of positive changes from said step f) to said number offibers emanating from said surface of said web substrate.
 2. The methodof claim 1 further comprising the step of providing an edge of said websubstrate as said two-dimensional image.
 3. The method of claim 2wherein said step of providing an edge further comprises the step offolding said web substrate.
 4. The method of claim 1 further comprisingthe step of providing said two-dimensional image as a grey-scale image.5. The method of claim 4 further comprising the step of providing saidgrey-scale image as a scanned image of said web substrate disposed upona contrasting background.
 6. The method of claim 5 further comprisingthe step of providing said scanned image with a resolution of at leastabout 50 dpi.
 7. The method of claim 6 further comprising the step ofproviding said scanned image with a resolution of at least about 1200dpi.
 8. The method of claim 4 further comprising the step of providingsaid two-dimensional image as a digital file.
 9. The method of claim 1wherein said step of establishing said Z-direction baseline furthercomprises the step of converting said image file to a binary file. 10.The method of claim 9 further comprising the step of determining an edgefrom said binary file.
 11. The method of claim 1 wherein said step ofestablishing said Z-direction baseline further comprises the step ofestablishing said baseline generally parallel to a direction orthogonaland co-planar to said Z-direction.
 12. The method of claim 1 furthercomprising the step of providing said image file with an orientation.13. The method of claim 12 wherein said step of providing said imagefile with an orientation further comprises the step of providing saidweb substrate with an edge generally parallel to either said MD or saidCD.
 14. The method of claim 13 wherein said step of providing said websubstrate with an edge generally parallel to said MD or CD is providedprior to said step c).
 15. The method of claim 1 further comprising thestep of determining a threshold value.
 16. The method of claim 15further comprising the step of determining an average pixel intensity ofa region disposed below said Z-direction baseline.
 17. The method ofclaim 16 further comprising the step of comparing said threshold valueto said change of intensity between said first and second pixels. 18.The method of claim 1 further comprising the step of prior to step g),determining a threshold value from the determination of the number ofpositive changes in intensity.
 19. The method of claim 1 furthercomprising the steps of determining a pixel intensity of a third pixeldisposed a height, 2d, above said Z-direction baseline and along saidlength L, determining a pixel intensity of a fourth pixel disposedadjacent said third pixel, said fourth pixel being disposed said height,2d, above said Z-direction baseline and generally orthogonal to saidZ-direction, determining a change of intensity between said third pixeland said fourth pixel, determining a number of positive changes in saidchange of intensity between said third pixel and said fourth pixel,mathematically averaging said number of positive changes in said changeof intensity between said first pixel and said second pixel with saidnumber of positive changes in said change of intensity between saidthird pixel and said fourth pixel, and, correlating said mathematicallyaveraged number of positive changes in said change of intensity betweensaid first pixel and said second pixel with said number of positivechanges in said change of intensity between said third pixel and saidfourth pixel to said number of fibers emanating from said surface ofsaid web substrate.